User Profiling in Ego-network : Co-profiling Attributes and Relationships

The Database and Info. Systems Lab.University of Illinois at Urbana-Champaign

User Profiling in Ego-network:Co-profiling

Attributes and Relationships

Rui Li, Chi Wang, Kevin Chen-Chuan Chang

University of Illinois at Urbana-Champaign

User Profiling, which infers users’ attributes, is important for Personalized Services

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and many others.

Personalized Search

Targeted Advertisement

Search Engines

Advertisers

Richard

User

College: UIUCLocation:

Champaign

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User Profiling is crucial for Social Analysis– Ability to survey the world

Surveying people for behavior: How do college students like iPad vs. Galaxy? How do California age 50+ males like

ObamaCare?

Surveying behavior for people: What demographics of users like Samsung more

than Apple? What communities of people support ObamaCare?

Can we profile users’ missing attributes in social network?

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Some users provide attributes in their online profiles

Some users’ attributes are missing

Employer: Yahoo! College:

Stanford

Employer: ? College:?

Employer: Yahoo! College: Berkeley

Employer: Twitter College: Berkeley


Employer: Twitter College: UIUC

Employee: ? College:?

Employer: Google College: UIUC

Employee: JP Morgan College: UIUC


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Thus, we abstract our problem as profiling users' attributes based on friends’ attributes

Input: • a network G(V, E) ,• some users’

attributes

Output: • users’ attributes

Employer: Yahoo! College: Stanford






Employer: JP Morgan College: UIUC


Employer: Yahoo!College: UIUC

While attributes may “propagate” across links—Links are very noisy.

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Existing methods simply assume that two connected users share the same value for any attribute








Employer: JP Morgan College: UIUC


However, users connect to friends with different values for an attribute


• About 11% friends share the employer and 18% friends share the college.

• Only 20% may have attributes.

Why noisy? Every link is for a (different) relationship!

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Richard and Bob share the same employer, but may have different values

for other attributes.

Richard and Cindy share the same college, but may have different values for

other attributes.

Richard and Peter share the same interests, but may have different values

for other attributes.

Richard

BobColleagues

Cindy

Peter

College classmates

Club friends

Users have different types of relationships in real life.

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On the other hand, Relationship Profiling is necessary by itself, and similarly challenged! Link: Why does a link happen?

Given a link, what friendship does it represent?

Circle: Who form what circles? Where are my circles? What does each circle represent?

Challenge: While links/circles depend on attributes to detect and to explain, attributes are often unknown.

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Proposal: Co-profiling Attributes and Relationships Attributes– properties of nodes Relationships– properties of links Together, understanding both nodes and links.

Why together?

1. Necessity: Dependency on each other to decide.

2. Benefit: Useful to know both!

classmates Employer: Google

College: UIUC


colleaguesCollege: UIUC

Employer: Yahoo!

Missing Missing

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But how?Observing how attributes and relationships

relate.

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Insight: Correlation between attributes and connections through relationship

Discriminative Correlation Insight : Attributes and connections are discriminatively correlated via

a hidden factor -- relationship

To concretize our insight, we explore two dependencies based on a real-world user study.

• Attribute-Relationship Dependency: How users’ attributes are related to hidden relationship types?

• Connection-Relationship Dependency: How connections are related to hidden relationship types?

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Observation #1: Attribute-Relationship Dependency

Friends do not share all attributes. What attributes they share depend on relationship.

The percentages of friends sharing the same value with the ego for different attributes overall of different relationship

types.

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Observation #2: Connection-Relationship Dependency

Friends do not connect to all friends. What friends they connect to depend on relationship.

The average connections per user within and across three different relationships types

f3 =<1, 0, 0, 0, 1, 0, 0.1>

Specifically, we focus on co-profiling upon each user’s ego-network

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Ego-network: a subnet that around an individual user.

Circle1: friends likely to share employee

Circle 2: friends likely to share college

Circle 3: friends likely to share other attribute




Employee: Twitter College: Berkeley



Employer: ?

College:?


Employer: Yahoo

College: UIUC

Attribute Vectorf1 =<1, 0, 0, 1, 0, 0,

0.1>Circle

Assignmentx1=1

x3=1

Association Vector w1 =<1, 0, 0, 0, 0, 0, 0>w2 =<0, 1, 0, 0, 0, 0, 0>

f4 =<0, 1, 0, 0, 0, 1, 0 0.1>x4=2

Solution Overview: we realize co-profiling in an optimization framework

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Unobserved Friends’ circles

Observed User Connections

Partially ObservedUser Attributes

Cost Function: capture the dependences between the variables based on the insight

Algorithm: finds the unknown variable that best satisfy the dependences

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Cost Function: we design a cost function to model the dependencies between variables

Attribute-Relationship (circle) Dependency

Connection-Relationship Type (circle) Dependency

There are other formulas to model the dependencies.

However, the function can not be optimized directly, as there are both discrete and continuous

variables

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Algorithm: we minimize the function via updating each group of variables

Update User Attribute Vectors F

Update User Circle Assignments X

Update Circle Association Vectors W

• Only propagate values from friends in the same circles

• Only propagate the attribute value associated with the circle

• Cosider both user’s attributes and connections

• Make association vector sparse

Experiment: we first collect real-world ego-networks to evaluate our data set

We conduct user studies to collect users’ attributes and relationship types (circles) from LinkedIn.

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Ego Users Users Connections

175 19K 110K

We share the data online https://

wiki.engr.illinois.edu/display/forward/Dataset-EgoNetUIUC-LinkedinCrawl-Jan2014

• Most users are have three attributes • 8K connection are labeled

https://wiki.engr.illinois.edu/display/forward/Dataset-EgoNetUIUC-LinkedinCrawl-Jan2014



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Experiment: we evaluate our algorithm on both attribute and relationship type profiling Attribute Profiling

APw: a classic collective classification approach, which profiles a node’s label using weighted votes from its neighbors.

APi: anther collective classification (semi-supervised learning) approach, which iteratively profiles nodes’ labels with APw.

APc: a state-of-art method, which profiles users’ attributers based on clustering network.

Relationship Type (circle) profiling RPa: profiles friends’ circles based on their attributes. RPn: profiles friends’ circles based on network structure RPan: profiles friends’ circles based on network and attributes, but assumes

attributes known.

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CP is not only capable of profiling AP and RP and but also outperforms baselines for both

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0.7 Accuracy for College

APw APi APc CP0.48

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0.52

0.54

0.56

0.58

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0.62Accuracy for Employer

RPa RPn RPan CP0

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profiling college classmates

RPa RPn RPan CP0

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profiling colleagues

Summary: we made the following contributions in this problem

We propose a co-profiling approach that jointly profiles users’ attributes and relationship types (circles) in ego networks.

We present the discriminative correlation insight to capture the correlation between attributes and social connections.

We conduct extensive experiments to evaluate our algorithms on two tasks based on real-world ego networks.

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Thank You!

Documents

User Profiling in Ego-network : Co-profiling Attributes and Relationships